The research objective of this Faculty Early Career Development (CAREER) project is the development of a unified computational and theoretical framework that will bridge the gap between multi-period stochastic optimization models and computationally challenging practical large-scale applications in supply-chain and revenue management. Consider a firm like Amazon that has to manage over 40 million different items to satisfy customer purchases in a timely manner. Amazon has to make many daily decisions, such as how many units of each item to stock, where to locate inventories, and how to ship orders to customers. The management of this large supply chain is very challenging especially because the future customer demands and supplies are uncertain and fluctuate over time. Demand forecasts are one of the most effective tools in managing future uncertainties. However, how to use demand forecasts to devise an effective inventory control policy that matches supply and demand is a challenging problem both for researchers and practitioners. Another example is workforce revenue management optimization, where companies like IBM have to manage pools of skilled workers over time to handle multiple consulting projects, aiming at choosing the most profitable ones. Traditional modeling tools like dynamic programming are effective in studying structural properties of the optimal policies of some of these models. However, they typically don?t lead to efficient procedures to compute optimal or even good policies for practical instances. This research project seeks to develop a new algorithmic framework to study these problems under general modeling assumptions that capture their practical aspects. The new algorithms will be based on several new techniques, such as marginal cost-accounting schemes and cost-balancing techniques, and provide simple to implement, yet provably near-optimal policies. A key research methodology is the use of theoretical and computational performance analysis to guide the development of the new algorithms. Through collaborations with industry partners the algorithms will be tested on real data.
The proposal addresses several broad application domains in supply-chain and revenue management. If successful, the results of this research will lead to a unified modeling and algorithmic framework to study these practical problems in broader perspectives than the current state-of-the-art. This will expand the theoretical and computational understanding of these challenging problems. In the longer-term, the combination of more sophisticated models that capture the practical aspects of the problems together with conceptually simple and efficient algorithms is likely to lead to significant improvements in the performance and efficiencies of the respective supply chains and other business environments. Collaboration with industry partners will be used to enhance the practical impact of this research project, and to enrich the classroom experience for students.
